ebola_2014_2016_clean <- read.csv("D:/desktop/R COde/ebola/ebola_2014_2016_clean.csv")
View(ebola_2014_2016_clean)
Basic data cleaing
Seprating the original Date column into month , date and year columns.
Ebola<-ebola_2014_2016_clean
library(dplyr)
library(tidyr)
Ebola<-Ebola %>% separate(Date, c("Month", "Date","Year"))
Ebola <- Ebola[,-c(6,10)]
head(Ebola)
Arranging the dataset with respect to the countries.
Ebola%>% arrange(Country)
Changing the column names.
colnames(Ebola)[5:10]<- c("suspected.cases","confirmed.cases","total.cases","suspected.deaths","confirmed.deaths","total.deaths")
colnames(Ebola)
[1] "Country" "Month" "Date" "Year" "suspected.cases" "confirmed.cases" "total.cases"
[8] "suspected.deaths" "confirmed.deaths" "total.deaths"
Creating a list of all the countries that got affected from the outbreak
country_list<-c(distinct(Ebola,Country))
country_list
$Country
[1] Guinea Nigeria Sierra Leone Liberia Senegal USA Spain Mali United Kingdom
[10] Italy
Levels: Guinea Italy Liberia Mali Nigeria Senegal Sierra Leone Spain United Kingdom USA
Creating total cases vs total deaths plot for each country
1) Calculating total cases and deaths in Guinea
r1 <-Ebola%>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2014)
r2 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2015)
r3 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2016)
deaths_2014_Guinea <- sapply(select(r1,total.deaths),na.rm=T,max)
deaths_2015_Guinea<- sapply(select(r2,total.deaths),na.rm=T,max)-sapply(select(r1,total.deaths),na.rm=T,max)
deaths_2016_Guinea <- sapply(select(r3,total.deaths),na.rm=T,max)-sapply(select(r2,total.deaths),na.rm=T,max)
s1 <-Ebola%>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2014)
s2 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2015)
s3 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2016)
cases_2014_Guinea <- sapply(select(s1,total.cases),na.rm=T,max)
cases_2015_Guinea<- sapply(select(s2,total.cases),na.rm=T,max)-sapply(select(s1,total.cases),na.rm=T,max)
cases_2016_Guinea <- sapply(select(s3,total.cases),na.rm=T,max)-sapply(select(s2,total.cases),na.rm=T,max)
2)Calculating total cases and deaths in Nigeria
r4 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2014)
r5 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2015)
r6 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2016)
deaths_2014_Nigeria <- sapply(select(r4,total.deaths),na.rm=T,max)
deaths_2015_Nigeria<- sapply(select(r5,total.deaths),na.rm=T,max)-sapply(select(r4,total.deaths),na.rm=T,max)
deaths_2016_Nigeria <- sapply(select(r6,total.deaths),na.rm=T,max)-sapply(select(r5,total.deaths),na.rm=T,max)
s4 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2014)
s5 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2015)
s6 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2016)
cases_2014_Nigeria <- sapply(select(s4,total.cases),na.rm=T,max)
cases_2015_Nigeria<- sapply(select(s5,total.cases),na.rm=T,max)-sapply(select(s4,total.cases),na.rm=T,max)
cases_2016_Nigeria <- sapply(select(s6,total.cases),na.rm=T,max)-sapply(select(s5,total.cases),na.rm=T,max)
3)Calculating total cases and deaths in Sierra
r7 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2014)
r8 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2015)
r9 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2016)
deaths_2014_Sierra <- sapply(select(r7,total.deaths),na.rm=T,max)
deaths_2015_Sierra<- sapply(select(r8,total.deaths),na.rm=T,max)-sapply(select(r7,total.deaths),na.rm=T,max)
deaths_2016_Sierra <- sapply(select(r9,total.deaths),na.rm=T,max)-sapply(select(r8,total.deaths),na.rm=T,max)
s7 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2014)
s8 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2015)
s9 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2016)
cases_2014_Sierra <- sapply(select(s7,total.cases),na.rm=T,max)
cases_2015_Sierra<- sapply(select(s8,total.cases),na.rm=T,max)-sapply(select(s7,total.cases),na.rm=T,max)
cases_2016_Sierra <- sapply(select(s9,total.cases),na.rm=T,max)-sapply(select(s8,total.cases),na.rm=T,max)
4)Calculating total cases and deaths in Liberia
r10 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2014)
r11 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2015)
r12 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2016)
deaths_2014_Liberia <- sapply(select(r10,total.deaths),na.rm=T,max)
deaths_2015_Liberia<- sapply(select(r11,total.deaths),na.rm=T,max)-sapply(select(r10,total.deaths),na.rm=T,max)
deaths_2016_Liberia <- sapply(select(r12,total.deaths),na.rm=T,max)-sapply(select(r11,total.deaths),na.rm=T,max)
s10 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2014)
s11 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2015)
s12 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2016)
cases_2014_Liberia <- sapply(select(s10,total.cases),na.rm=T,max)
cases_2015_Liberia<- sapply(select(s11,total.cases),na.rm=T,max)-sapply(select(s10,total.cases),na.rm=T,max)
cases_2016_Liberia <- sapply(select(s12,total.cases),na.rm=T,max)-sapply(select(s11,total.cases),na.rm=T,max)
5)Calculating total cases and deaths in Senegal
r13 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2014)
r14 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2015)
r15 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2016)
deaths_2014_Senegal <- sapply(select(r13,total.deaths),na.rm=T,max)
deaths_2015_Senegal<- sapply(select(r14,total.deaths),na.rm=T,max)-sapply(select(r13,total.deaths),na.rm=T,max)
deaths_2016_Senegal <- sapply(select(r15,total.deaths),na.rm=T,max)-sapply(select(r14,total.deaths),na.rm=T,max)
s13 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2014)
s14 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2015)
s15 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2016)
cases_2014_Senegal <- sapply(select(s13,total.cases),na.rm=T,max)
cases_2015_Senegal<- sapply(select(s14,total.cases),na.rm=T,max)-sapply(select(s13,total.cases),na.rm=T,max)
cases_2016_Senegal <- sapply(select(s15,total.cases),na.rm=T,max)-sapply(select(s14,total.cases),na.rm=T,max)
6)Calculating total cases and deaths in USA
r16 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2014)
r17 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2015)
r18 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2016)
deaths_2014_USA<- sapply(select(r16,total.deaths),na.rm=T,max)
deaths_2015_USA<- sapply(select(r17,total.deaths),na.rm=T,max)-sapply(select(r15,total.deaths),na.rm=T,max)
deaths_2016_USA <- sapply(select(r18,total.deaths),na.rm=T,max)-sapply(select(r17,total.deaths),na.rm=T,max)
s16 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2014)
s17 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2015)
s18 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2016)
cases_2014_USA<- sapply(select(s16,total.cases),na.rm=T,max)
cases_2015_USA<- sapply(select(s17,total.cases),na.rm=T,max)-sapply(select(s15,total.cases),na.rm=T,max)
cases_2016_USA <- sapply(select(s18,total.cases),na.rm=T,max)-sapply(select(s17,total.cases),na.rm=T,max)
7)Calculating total cases and deaths in Spain
r19 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2014)
r20 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2015)
r21 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2016)
deaths_2014_Spain <- sapply(select(r19,total.deaths),na.rm=T,max)
deaths_2015_Spain<- sapply(select(r20,total.deaths),na.rm=T,max)-sapply(select(r19,total.deaths),na.rm=T,max)
deaths_2016_Spain <- sapply(select(r21,total.deaths),na.rm=T,max)-sapply(select(r20,total.deaths),na.rm=T,max)
s19 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2014)
s20 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2015)
s21 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2016)
cases_2014_Spain <- sapply(select(s19,total.cases),na.rm=T,max)
cases_2015_Spain<- sapply(select(s20,total.cases),na.rm=T,max)-sapply(select(s19,total.cases),na.rm=T,max)
cases_2016_Spain <- sapply(select(s21,total.cases),na.rm=T,max)-sapply(select(s20,total.cases),na.rm=T,max)
- calculating total cases and deaths in Mali
r22 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2014)
r23 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2015)
r24 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2016)
deaths_2014_Mali <- sapply(select(r22,total.deaths),na.rm=T,max)
deaths_2015_Mali<- sapply(select(r23,total.deaths),na.rm=T,max)-sapply(select(r22,total.deaths),na.rm=T,max)
deaths_2016_Mali <- sapply(select(r24,total.deaths),na.rm=T,max)-sapply(select(r23,total.deaths),na.rm=T,max)
s22 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2014)
s23 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2015)
s24 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2016)
cases_2014_Mali <- sapply(select(s22,total.cases),na.rm=T,max)
cases_2015_Mali<- sapply(select(s23,total.cases),na.rm=T,max)-sapply(select(s22,total.cases),na.rm=T,max)
cases_2016_Mali <- sapply(select(s24,total.cases),na.rm=T,max)-sapply(select(s23,total.cases),na.rm=T,max)
- calculating total cases and deaths in United kingdom
r25 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2014)
r26 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2015)
r27 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2016)
deaths_2014_UnitedKingdom <- sapply(select(r25,total.deaths),na.rm=T,max)
deaths_2015_UnitedKingdom <- sapply(select(r26,total.deaths),na.rm=T,max)-sapply(select(r25,total.deaths),na.rm=T,max)
deaths_2016_UnitedKingdom <- sapply(select(r26,total.deaths),na.rm=T,max)-sapply(select(r26,total.deaths),na.rm=T,max)
s25 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2014)
s26 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2015)
s27 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2016)
cases_2014_UnitedKingdom <- sapply(select(s25,total.cases),na.rm=T,max)
cases_2015_UnitedKingdom <- sapply(select(s26,total.cases),na.rm=T,max)-sapply(select(s25,total.cases),na.rm=T,max)
cases_2016_UnitedKingdom <- sapply(select(s26,total.cases),na.rm=T,max)-sapply(select(s26,total.cases),na.rm=T,max)
- calculating total cases and deaths in United kingdom
r28 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2014)
r29 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2015)
r30 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2016)
deaths_2014_Italy <- sapply(select(r28,total.deaths),na.rm=T,max)
deaths_2015_Italy<- sapply(select(r29,total.deaths),na.rm=T,max)-sapply(select(r28,total.deaths),na.rm=T,max)
deaths_2016_Italy <- sapply(select(r30,total.deaths),na.rm=T,max)-sapply(select(r29,total.deaths),na.rm=T,max)
s28 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2014)
s29 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2015)
s30 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2016)
cases_2014_Italy <- sapply(select(s28,total.cases),na.rm=T,max)
cases_2015_Italy<- sapply(select(s29,total.cases),na.rm=T,max)-sapply(select(s28,total.cases),na.rm=T,max)
cases_2016_Italy <- sapply(select(s30,total.cases),na.rm=T,max)-sapply(select(s29,total.cases),na.rm=T,max)
Creating a seperate dataframe for deaths in each year
deaths_2014 <- c(deaths_2014_Guinea,deaths_2014_Nigeria,deaths_2014_Sierra,deaths_2014_Liberia,deaths_2014_Senegal,deaths_2014_USA,
deaths_2014_Spain,deaths_2014_Mali,deaths_2014_UnitedKingdom ,deaths_2014_Italy )
deaths_2015 <- c(deaths_2015_Guinea,deaths_2015_Nigeria,deaths_2015_Sierra,deaths_2015_Liberia,deaths_2015_Senegal,deaths_2015_USA,
deaths_2015_Spain,deaths_2015_Mali,deaths_2015_UnitedKingdom ,deaths_2015_Italy )
deaths_2016 <- c(deaths_2016_Guinea,deaths_2016_Nigeria,deaths_2016_Sierra,deaths_2016_Liberia,deaths_2016_Senegal,deaths_2016_USA,
deaths_2016_Spain,deaths_2016_Mali,deaths_2016_UnitedKingdom,deaths_2016_Italy )
death.Ebola <- data.frame(country_list,deaths_2014,deaths_2015,deaths_2016)
death.Ebola <- do.call(data.frame, lapply(death.Ebola, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
invalid factor level, NA generated
death.Ebola
Creating a dataframe for cases in each year
cases_2014 <- c(cases_2014_Guinea,cases_2014_Nigeria,cases_2014_Sierra,cases_2014_Liberia,cases_2014_Senegal,cases_2014_USA,cases_2014_Spain,
cases_2014_Mali,cases_2014_UnitedKingdom ,cases_2014_Italy )
cases_2015 <- c(cases_2015_Guinea,cases_2015_Nigeria,cases_2015_Sierra,cases_2015_Liberia,cases_2015_Senegal,cases_2015_USA,cases_2015_Spain,
cases_2015_Mali,cases_2015_UnitedKingdom ,cases_2015_Italy )
cases_2016 <- c(cases_2016_Guinea,cases_2016_Nigeria,cases_2016_Sierra,cases_2016_Liberia,cases_2016_Senegal,cases_2016_USA,cases_2016_Spain,
cases_2016_Mali,cases_2016_UnitedKingdom,cases_2016_Italy )
case.Ebola <- data.frame(country_list,cases_2014,cases_2015,cases_2016)
case.Ebola <- do.call(data.frame, lapply(case.Ebola, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
invalid factor level, NA generated
case.Ebola
case.Ebola$cases_2015[case.Ebola$cases_2015 ==-2] <- 0
case.Ebola$cases_2016[case.Ebola$cases_2016 ==-6] <- 0
case.Ebola
NA
NA
Plot for total deaths in year 2014
library(ggplot2)
library(plotly)
pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2014))
pl2 <- pl+geom_bar(stat = 'identity',fill = '#fc0373', alpha= 0.95)
pl3 <- pl2 + xlab('Country')+ylab('No of deaths in 2014 ')
pl4<- pl3+ggtitle(" Number of deaths in year 2014 due to outbreak of Ebola virus")
print( pl4 )

Plot for total deaths in year 2015
pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2015))
pl2 <- pl+geom_bar(stat = 'identity',fill = '#a3cc31', alpha= 0.95)
pl3 <- pl2 + xlab('Country')+ylab('No of deaths ')
pl4<- pl3+ggtitle(" Number of deaths in year 2015 due to outbreak of Ebola virus")
print( pl4 )

Plot for total deaths in year 2015
Since, there were no deaths recoreded in 2016, our plot shows nothing.
pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2016))
pl2 <- pl+geom_bar(stat = 'identity',fill = '#cc314d', alpha= 0.95)
pl3 <- pl2 + xlab('Country')+ylab('No of deaths ')
pl4<- pl3+ggtitle(" Number of deaths in year 2016 due to outbreak of Ebola virus")
gpl<- ggplotly(pl4)
print( gpl )
NULL
Plot for cases in 2014
pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2014))
pl2 <- pl+geom_bar(stat = 'identity',fill='#cc314d',alpha= 0.95)
pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')
pl4<- pl3+ggtitle(" Number of Cases in year 2014 due to outbreak of Ebola virus ")
gpl<- ggplotly(pl4)
print( pl4 )

Plot for cases in 2015
pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2015))
pl2 <- pl+geom_bar(stat = 'identity',fill='#fa02cd',alpha= 0.95)
pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')
pl4<- pl3+ggtitle(" Number of Cases in year 2015 due to outbreak of Ebola virus ")
gpl<- ggplotly(pl4)
print( pl4 )

Plot for cases in 2016
Since, no cases were recorded in 2016, our plot shows nothing
pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2016))
pl2 <- pl+geom_bar(stat = 'identity',fill='#fa02cd',alpha= 0.95)
pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')
pl4<- pl3+ggtitle(" Number of Cases in year 2016 due to outbreak of Ebola virus(2014-2016) ")
gpl<- ggplotly(pl4)
print( pl4 )

Plot for suspected Vs confirmed deaths during the outbreak
library(ggplot2)
library(plotly)
pl <- ggplot(Ebola, aes(x=total.deaths, y=total.cases ))
pl2 <- pl+geom_smooth(model=lm,color="Blue", fill="Red")
Ignoring unknown parameters: model
pl3 <- pl2 + xlab('Total deaths')+ylab("Total cases")+ggtitle("Plot for total cases vs total deaths")
gpl<- ggplotly(pl3)
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Removed 8 rows containing non-finite values (stat_smooth).
print(pl3)

Plot for total deaths due Ebola virus
library(ggplot2)
library(plotly)
pl <- ggplot(Ebola, aes(y=total.deaths,x= Year))
pl2 <- pl+geom_bar(stat = "identity",color="red")
pl3 <- pl2 + xlab('Year')+ylab("Total deaths")+ggtitle("Plot for total deaths")
gpl<- ggplotly(pl3)
print(gpl)
NULL
Plot for total Cases
library(ggplot2)
library(plotly)
pl <- ggplot(Ebola, aes(y=total.cases,x= Year))
pl2 <- pl+geom_bar(stat = "identity",color="red")
pl3 <- pl2 + xlab('Year')+ylab("Total cases")+ggtitle("Plot for total cases")
gpl<- ggplotly(pl3)
Removed 8 rows containing missing values (position_stack).
print(pl3)

df.2014 <- data.frame(country_list,case.Ebola$cases_2014,death.Ebola$deaths_2014)
df.2014 <- do.call(data.frame, lapply(df.2014, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
invalid factor level, NA generated
df.2014
NA
Pie chart for mortality rate in year 2014
death.percentage.2014 = c(sum(df.2014$deaths_2014)/sum(df.2014$cases_2014), (sum(df.2014$cases_2014) - sum(df.2014$deaths_2014))/sum(df.2014$cases_2014))
death.percentage.2014
[1] 0.3911624 0.6088376
library(plotrix)
Group <- c("Death%","Recorved%")
lbls <- paste0(Group, " ", round(death.percentage.2014 / sum(death.percentage.2014) * 100, 1), "%")
pie3D(death.percentage.2014,labels=lbls,explode=0.1,
main="Death Vs Recorverd in 2014 ")

df.2015 <- data.frame(country_list,case.Ebola$cases_2015,death.Ebola$deaths_2015)
df.2015 <- do.call(data.frame, lapply(df.2015, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
invalid factor level, NA generated
print(df.2015)
NA
death.percentage.2015 = c(sum(df.2015$deaths_2015)/sum(df.2015$cases_2015), (sum(df.2015$cases_2015) - sum(df.2015$deaths_2015))/sum(df.2015$cases_2015))
print(death.percentage.2015)
[1] 0.4044624 0.5955376
Pie chart for mortality rate in year 2015
library(plotrix)
Group <- c("Death%","Recorved%")
lbls <- paste0(Group, " ", round(death.percentage.2015 / sum(death.percentage.2015) * 100, 1), "%")
pie3D(death.percentage.2015,labels=lbls,explode=0.1,main="Death Vs Recorverd in 2015 ")

df.2016 <- data.frame(country_list,case.Ebola$cases_2016,death.Ebola$deaths_2016)
df.2016 <- do.call(data.frame, lapply(df.2016, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
invalid factor level, NA generated
print(df.2016)
death.percentage.2016 = c(sum(df.2016$deaths_2016)/sum(df.2016$cases_2016), (sum(df.2016$cases_2016) - sum(df.2016$deaths_2016))/sum(df.2016$cases_2016))
print(death.percentage.2016)
[1] 0 1
Pie chart for mortality rate in year 2016
library(plotrix)
lbls <- c("Recorved 100%")
pie3D(death.percentage.2016,labels=lbls,main="Death Vs Recorverd in 2016 ")

death.percentage = c(sum(Ebola$total.deaths)/sum(Ebola$total.cases), (sum(Ebola$total.cases) - sum(Ebola$total.deaths))/sum(Ebola$total.cases))
death.percentage
[1] 0.4039933 0.5960067
Pie chart for total mortality rate during the Ebola Outbreak(2014- 2016)
library(plotrix)
Group <- c("Death%","Recorved%")
lbls <- paste0(Group, " ", round(death.percentage / sum(death.percentage) * 100, 1), "%")
pie3D(death.percentage,labels=lbls,explode=0.1,main="Death Vs Recorverd (2014,2015 and 2016) ")

---
title: "Ebola outbreak 2014- 2016"
output: html_notebook
---

```{r}
ebola_2014_2016_clean <- read.csv("D:/desktop/R COde/ebola/ebola_2014_2016_clean.csv")

View(ebola_2014_2016_clean)
```
<font size="5"> Basic data cleaing </font>


<font size = "2"> Seprating the original Date column into month , date and year columns.
```{r}
Ebola<-ebola_2014_2016_clean
library(dplyr)
library(tidyr)
Ebola<-Ebola %>% separate(Date, c("Month", "Date","Year"))
Ebola <- Ebola[,-c(6,10)]

head(Ebola)
```
Arranging the dataset with respect to the countries.
```{r}
Ebola%>% arrange(Country)
```
Changing the column names.
```{r}
colnames(Ebola)[5:10]<- c("suspected.cases","confirmed.cases","total.cases","suspected.deaths","confirmed.deaths","total.deaths")

colnames(Ebola)
```
Creating a list of all the countries that got affected from the outbreak </font>
```{r}
country_list<-c(distinct(Ebola,Country))

country_list
```
<font size="5"> Creating total cases vs total deaths plot for each country</font>

<font size ="3">1) Calculating total cases and deaths in Guinea
```{r}
r1 <-Ebola%>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2014) 
r2 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2015) 
r3 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2016) 

deaths_2014_Guinea <- sapply(select(r1,total.deaths),na.rm=T,max)
deaths_2015_Guinea<- sapply(select(r2,total.deaths),na.rm=T,max)-sapply(select(r1,total.deaths),na.rm=T,max)
deaths_2016_Guinea <- sapply(select(r3,total.deaths),na.rm=T,max)-sapply(select(r2,total.deaths),na.rm=T,max)


s1 <-Ebola%>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2014) 
s2 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2015) 
s3 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2016) 

cases_2014_Guinea <- sapply(select(s1,total.cases),na.rm=T,max)
cases_2015_Guinea<- sapply(select(s2,total.cases),na.rm=T,max)-sapply(select(s1,total.cases),na.rm=T,max)
cases_2016_Guinea <- sapply(select(s3,total.cases),na.rm=T,max)-sapply(select(s2,total.cases),na.rm=T,max)
```
2)Calculating total cases and deaths in Nigeria
```{r}
r4 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2014) 
r5 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2015) 
r6 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2016) 

deaths_2014_Nigeria <- sapply(select(r4,total.deaths),na.rm=T,max)
deaths_2015_Nigeria<- sapply(select(r5,total.deaths),na.rm=T,max)-sapply(select(r4,total.deaths),na.rm=T,max)
deaths_2016_Nigeria <- sapply(select(r6,total.deaths),na.rm=T,max)-sapply(select(r5,total.deaths),na.rm=T,max)


s4 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2014) 
s5 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2015) 
s6 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2016) 

cases_2014_Nigeria <- sapply(select(s4,total.cases),na.rm=T,max)
cases_2015_Nigeria<- sapply(select(s5,total.cases),na.rm=T,max)-sapply(select(s4,total.cases),na.rm=T,max)
cases_2016_Nigeria <- sapply(select(s6,total.cases),na.rm=T,max)-sapply(select(s5,total.cases),na.rm=T,max)

```
3)Calculating total cases and deaths in Sierra
```{r}
r7 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2014) 
r8 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2015) 
r9 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2016) 

deaths_2014_Sierra <- sapply(select(r7,total.deaths),na.rm=T,max)
deaths_2015_Sierra<- sapply(select(r8,total.deaths),na.rm=T,max)-sapply(select(r7,total.deaths),na.rm=T,max)
deaths_2016_Sierra <- sapply(select(r9,total.deaths),na.rm=T,max)-sapply(select(r8,total.deaths),na.rm=T,max)

s7 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2014) 
s8 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2015) 
s9 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2016) 

cases_2014_Sierra <- sapply(select(s7,total.cases),na.rm=T,max)
cases_2015_Sierra<- sapply(select(s8,total.cases),na.rm=T,max)-sapply(select(s7,total.cases),na.rm=T,max)
cases_2016_Sierra <- sapply(select(s9,total.cases),na.rm=T,max)-sapply(select(s8,total.cases),na.rm=T,max)

```
4)Calculating total cases and deaths in Liberia
```{r}
r10 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2014) 
r11 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2015) 
r12 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2016) 

deaths_2014_Liberia <- sapply(select(r10,total.deaths),na.rm=T,max)
deaths_2015_Liberia<- sapply(select(r11,total.deaths),na.rm=T,max)-sapply(select(r10,total.deaths),na.rm=T,max)
deaths_2016_Liberia <- sapply(select(r12,total.deaths),na.rm=T,max)-sapply(select(r11,total.deaths),na.rm=T,max)


s10 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2014) 
s11 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2015) 
s12 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2016) 

cases_2014_Liberia <- sapply(select(s10,total.cases),na.rm=T,max)
cases_2015_Liberia<- sapply(select(s11,total.cases),na.rm=T,max)-sapply(select(s10,total.cases),na.rm=T,max)
cases_2016_Liberia <- sapply(select(s12,total.cases),na.rm=T,max)-sapply(select(s11,total.cases),na.rm=T,max)
```
5)Calculating total cases and deaths in Senegal
```{r}
r13 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2014) 
r14 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2015) 
r15 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2016)

deaths_2014_Senegal <- sapply(select(r13,total.deaths),na.rm=T,max)
deaths_2015_Senegal<- sapply(select(r14,total.deaths),na.rm=T,max)-sapply(select(r13,total.deaths),na.rm=T,max)
deaths_2016_Senegal <- sapply(select(r15,total.deaths),na.rm=T,max)-sapply(select(r14,total.deaths),na.rm=T,max)


s13 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2014) 
s14 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2015) 
s15 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2016)

cases_2014_Senegal <- sapply(select(s13,total.cases),na.rm=T,max)
cases_2015_Senegal<- sapply(select(s14,total.cases),na.rm=T,max)-sapply(select(s13,total.cases),na.rm=T,max)
cases_2016_Senegal <- sapply(select(s15,total.cases),na.rm=T,max)-sapply(select(s14,total.cases),na.rm=T,max)
```
6)Calculating total cases and deaths in USA
```{r}
r16 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2014) 
r17 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2015) 
r18 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2016) 

deaths_2014_USA<- sapply(select(r16,total.deaths),na.rm=T,max)
deaths_2015_USA<- sapply(select(r17,total.deaths),na.rm=T,max)-sapply(select(r15,total.deaths),na.rm=T,max)
deaths_2016_USA <- sapply(select(r18,total.deaths),na.rm=T,max)-sapply(select(r17,total.deaths),na.rm=T,max)



s16 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2014) 
s17 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2015) 
s18 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2016) 

cases_2014_USA<- sapply(select(s16,total.cases),na.rm=T,max)
cases_2015_USA<- sapply(select(s17,total.cases),na.rm=T,max)-sapply(select(s15,total.cases),na.rm=T,max)
cases_2016_USA <- sapply(select(s18,total.cases),na.rm=T,max)-sapply(select(s17,total.cases),na.rm=T,max)

```
7)Calculating total cases and deaths in Spain
```{r}
r19 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2014) 
r20 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2015) 
r21 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2016) 

deaths_2014_Spain <- sapply(select(r19,total.deaths),na.rm=T,max)
deaths_2015_Spain<- sapply(select(r20,total.deaths),na.rm=T,max)-sapply(select(r19,total.deaths),na.rm=T,max)
deaths_2016_Spain <- sapply(select(r21,total.deaths),na.rm=T,max)-sapply(select(r20,total.deaths),na.rm=T,max)


s19 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2014) 
s20 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2015) 
s21 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2016) 

cases_2014_Spain <- sapply(select(s19,total.cases),na.rm=T,max)
cases_2015_Spain<- sapply(select(s20,total.cases),na.rm=T,max)-sapply(select(s19,total.cases),na.rm=T,max)
cases_2016_Spain <- sapply(select(s21,total.cases),na.rm=T,max)-sapply(select(s20,total.cases),na.rm=T,max)
```
8) calculating total cases and deaths in Mali
```{r}
r22 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2014) 
r23 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2015) 
r24 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2016) 

deaths_2014_Mali <- sapply(select(r22,total.deaths),na.rm=T,max)
deaths_2015_Mali<- sapply(select(r23,total.deaths),na.rm=T,max)-sapply(select(r22,total.deaths),na.rm=T,max)
deaths_2016_Mali <- sapply(select(r24,total.deaths),na.rm=T,max)-sapply(select(r23,total.deaths),na.rm=T,max)


s22 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2014) 
s23 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2015) 
s24 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2016) 

cases_2014_Mali <- sapply(select(s22,total.cases),na.rm=T,max)
cases_2015_Mali<- sapply(select(s23,total.cases),na.rm=T,max)-sapply(select(s22,total.cases),na.rm=T,max)
cases_2016_Mali <- sapply(select(s24,total.cases),na.rm=T,max)-sapply(select(s23,total.cases),na.rm=T,max)
```
9) calculating total cases and deaths in United kingdom
```{r}
r25 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2014) 
r26 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2015) 
r27 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2016) 

deaths_2014_UnitedKingdom <- sapply(select(r25,total.deaths),na.rm=T,max)
deaths_2015_UnitedKingdom <- sapply(select(r26,total.deaths),na.rm=T,max)-sapply(select(r25,total.deaths),na.rm=T,max)
deaths_2016_UnitedKingdom <- sapply(select(r26,total.deaths),na.rm=T,max)-sapply(select(r26,total.deaths),na.rm=T,max)



s25 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2014) 
s26 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2015) 
s27 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2016) 

cases_2014_UnitedKingdom <- sapply(select(s25,total.cases),na.rm=T,max)
cases_2015_UnitedKingdom <- sapply(select(s26,total.cases),na.rm=T,max)-sapply(select(s25,total.cases),na.rm=T,max)
cases_2016_UnitedKingdom <- sapply(select(s26,total.cases),na.rm=T,max)-sapply(select(s26,total.cases),na.rm=T,max)

```
10) calculating total cases and deaths in United kingdom</font>
```{r}
r28 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2014) 
r29 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2015) 
r30 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2016) 

deaths_2014_Italy <- sapply(select(r28,total.deaths),na.rm=T,max)
deaths_2015_Italy<- sapply(select(r29,total.deaths),na.rm=T,max)-sapply(select(r28,total.deaths),na.rm=T,max)
deaths_2016_Italy <- sapply(select(r30,total.deaths),na.rm=T,max)-sapply(select(r29,total.deaths),na.rm=T,max)

s28 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2014) 
s29 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2015) 
s30 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2016) 

cases_2014_Italy <- sapply(select(s28,total.cases),na.rm=T,max)
cases_2015_Italy<- sapply(select(s29,total.cases),na.rm=T,max)-sapply(select(s28,total.cases),na.rm=T,max)
cases_2016_Italy <- sapply(select(s30,total.cases),na.rm=T,max)-sapply(select(s29,total.cases),na.rm=T,max)

```
<font size = "4"> Creating a seperate dataframe for deaths in each year </font>
```{r}
deaths_2014 <- c(deaths_2014_Guinea,deaths_2014_Nigeria,deaths_2014_Sierra,deaths_2014_Liberia,deaths_2014_Senegal,deaths_2014_USA,
                 deaths_2014_Spain,deaths_2014_Mali,deaths_2014_UnitedKingdom ,deaths_2014_Italy    )


deaths_2015 <- c(deaths_2015_Guinea,deaths_2015_Nigeria,deaths_2015_Sierra,deaths_2015_Liberia,deaths_2015_Senegal,deaths_2015_USA,
                 deaths_2015_Spain,deaths_2015_Mali,deaths_2015_UnitedKingdom ,deaths_2015_Italy    )


deaths_2016 <- c(deaths_2016_Guinea,deaths_2016_Nigeria,deaths_2016_Sierra,deaths_2016_Liberia,deaths_2016_Senegal,deaths_2016_USA,
                 deaths_2016_Spain,deaths_2016_Mali,deaths_2016_UnitedKingdom,deaths_2016_Italy    )

death.Ebola <- data.frame(country_list,deaths_2014,deaths_2015,deaths_2016)
death.Ebola <- do.call(data.frame, lapply(death.Ebola, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

death.Ebola
```
<font size = "4">Creating a dataframe for cases in each year</font>
```{r}

cases_2014 <- c(cases_2014_Guinea,cases_2014_Nigeria,cases_2014_Sierra,cases_2014_Liberia,cases_2014_Senegal,cases_2014_USA,cases_2014_Spain,
                 cases_2014_Mali,cases_2014_UnitedKingdom ,cases_2014_Italy    )


cases_2015 <- c(cases_2015_Guinea,cases_2015_Nigeria,cases_2015_Sierra,cases_2015_Liberia,cases_2015_Senegal,cases_2015_USA,cases_2015_Spain,
                 cases_2015_Mali,cases_2015_UnitedKingdom ,cases_2015_Italy    )


cases_2016 <- c(cases_2016_Guinea,cases_2016_Nigeria,cases_2016_Sierra,cases_2016_Liberia,cases_2016_Senegal,cases_2016_USA,cases_2016_Spain,
                 cases_2016_Mali,cases_2016_UnitedKingdom,cases_2016_Italy    )



case.Ebola <- data.frame(country_list,cases_2014,cases_2015,cases_2016)
case.Ebola <- do.call(data.frame, lapply(case.Ebola, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

case.Ebola

case.Ebola$cases_2015[case.Ebola$cases_2015 ==-2] <- 0
case.Ebola$cases_2016[case.Ebola$cases_2016 ==-6] <- 0
case.Ebola


```
<font size = " 5"> Plot for total deaths in year 2014</font>

```{r}
library(ggplot2)
library(plotly)
pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2014))

pl2 <- pl+geom_bar(stat = 'identity',fill = '#fc0373', alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of deaths in 2014 ')

pl4<- pl3+ggtitle(" Number of deaths in year 2014 due to outbreak of Ebola virus")

print( pl4 )
```
<font size = " 5"> Plot for total deaths in year 2015</font>

```{r}

pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2015))

pl2 <- pl+geom_bar(stat = 'identity',fill = '#a3cc31', alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of deaths ')

pl4<- pl3+ggtitle(" Number of deaths in year 2015 due to outbreak of Ebola virus")

print( pl4 )
```
<font size = " 5">Plot for total deaths in year 2015</font>

<font size ="3"> Since, there were no deaths recoreded in 2016, our plot shows nothing.</font>
```{r}
pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2016))

pl2 <- pl+geom_bar(stat = 'identity',fill = '#cc314d', alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of deaths ')

pl4<- pl3+ggtitle(" Number of deaths in year 2016 due to outbreak of Ebola virus")

gpl<- ggplotly(pl4)

print( pl4 )

```
<font size = " 5">Plot for cases in 2014</font>
```{r}

pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2014))

pl2 <- pl+geom_bar(stat = 'identity',fill='#cc314d',alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')

pl4<- pl3+ggtitle(" Number of Cases in year 2014 due to outbreak of Ebola virus ")

gpl<- ggplotly(pl4)

print( pl4 )
```

<font size = " 5">Plot for cases in 2015</font>
```{r}
pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2015))

pl2 <- pl+geom_bar(stat = 'identity',fill='#fa02cd',alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')

pl4<- pl3+ggtitle(" Number of Cases in year 2015 due to outbreak of Ebola virus ")

gpl<- ggplotly(pl4)

print( pl4 )
```
<font size = " 5">Plot for cases in 2016</font>

<font size = " 3"> Since, no cases were recorded in 2016, our plot shows nothing</font>
```{r}
pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2016))

pl2 <- pl+geom_bar(stat = 'identity',fill='#fa02cd',alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')

pl4<- pl3+ggtitle(" Number of Cases in year 2016 due to outbreak of Ebola virus(2014-2016) ")

gpl<- ggplotly(pl4)

print( pl4 )
```
<font size = " 5">Plot for suspected Vs confirmed deaths during the outbreak</font>
```{r}
library(ggplot2)
library(plotly)

pl <- ggplot(Ebola, aes(x=total.deaths, y=total.cases ))

pl2 <- pl+geom_smooth(model=lm,color="Blue", fill="Red")
pl3 <- pl2 + xlab('Total deaths')+ylab("Total cases")+ggtitle("Plot for total cases vs total deaths")

gpl<- ggplotly(pl3)
print(pl3)
```
<font size = " 5">Plot for total deaths due Ebola virus</font>
```{r}
library(ggplot2)
library(plotly)

pl <- ggplot(Ebola, aes(y=total.deaths,x= Year))

pl2 <- pl+geom_bar(stat = "identity",color="red") 
pl3 <- pl2 + xlab('Year')+ylab("Total deaths")+ggtitle("Plot for  total deaths")

gpl<- ggplotly(pl3)
print(gpl)
```
<font size = " 5">Plot for total Cases</font>
```{r}
library(ggplot2)
library(plotly)

pl <- ggplot(Ebola, aes(y=total.cases,x= Year))

pl2 <- pl+geom_bar(stat = "identity",color="red") 
pl3 <- pl2 + xlab('Year')+ylab("Total cases")+ggtitle("Plot for  total cases")

gpl<- ggplotly(pl3)
print(pl3)
```


```{r}
df.2014 <-  data.frame(country_list,case.Ebola$cases_2014,death.Ebola$deaths_2014)
df.2014 <- do.call(data.frame, lapply(df.2014, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
 df.2014

```
<font size = " 5">Pie chart for mortality rate in year 2014</font>

```{r}
death.percentage.2014 = c(sum(df.2014$deaths_2014)/sum(df.2014$cases_2014), (sum(df.2014$cases_2014) - sum(df.2014$deaths_2014))/sum(df.2014$cases_2014))
death.percentage.2014
```
```{r}
library(plotrix)
Group <- c("Death%","Recorved%")

lbls <- paste0(Group, " ", round(death.percentage.2014 / sum(death.percentage.2014) * 100, 1), "%")
pie3D(death.percentage.2014,labels=lbls,explode=0.1,
   main="Death Vs Recorverd in 2014 ")
```
```{r}
df.2015 <-  data.frame(country_list,case.Ebola$cases_2015,death.Ebola$deaths_2015)
df.2015 <- do.call(data.frame, lapply(df.2015, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
 print(df.2015)
 
```
```{r}
death.percentage.2015 = c(sum(df.2015$deaths_2015)/sum(df.2015$cases_2015), (sum(df.2015$cases_2015) - sum(df.2015$deaths_2015))/sum(df.2015$cases_2015))
print(death.percentage.2015)

```
<font size = " 5">Pie chart for mortality rate in year 2015</font>
```{r}
library(plotrix)
Group <- c("Death%","Recorved%")

lbls <- paste0(Group, " ", round(death.percentage.2015 / sum(death.percentage.2015) * 100, 1), "%")
pie3D(death.percentage.2015,labels=lbls,explode=0.1,main="Death Vs Recorverd in 2015 ")
```

```{r}
df.2016 <-  data.frame(country_list,case.Ebola$cases_2016,death.Ebola$deaths_2016)
df.2016 <- do.call(data.frame, lapply(df.2016, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))
 print(df.2016)
```
```{r}
death.percentage.2016 = c(sum(df.2016$deaths_2016)/sum(df.2016$cases_2016), (sum(df.2016$cases_2016) - sum(df.2016$deaths_2016))/sum(df.2016$cases_2016))
print(death.percentage.2016)

```
<font size = " 5">Pie chart for mortality rate in year 2016</font>
```{r}
library(plotrix)
lbls <- c("Recorved 100%")
pie3D(death.percentage.2016,labels=lbls,main="Death Vs Recorverd in 2016 ")
```
```{r}
death.percentage = c(sum(Ebola$total.deaths)/sum(Ebola$total.cases), (sum(Ebola$total.cases) - sum(Ebola$total.deaths))/sum(Ebola$total.cases))
death.percentage
```
<font size = " 5">Pie chart for total mortality rate during the Ebola Outbreak(2014- 2016)</font>
```{r}
library(plotrix)
Group <- c("Death%","Recorved%")

lbls <- paste0(Group, " ", round(death.percentage / sum(death.percentage) * 100, 1), "%")
pie3D(death.percentage,labels=lbls,explode=0.1,main="Death Vs Recorverd (2014,2015 and 2016) ")
```


